The forces which affect homelessness are complex and often interactive in nature. Social forces such as addictions, family breakdown, and mental illness are compounded by structural forces such as lack of available low-cost housing, poor economic conditions, and insufficient mental health services. Together these factors impact levels of homelessness through their dynamic relations. Historic models, which are static in nature, have only been marginally successful in capturing these relationships.
Fuzzy Logic (FL) and fuzzy cognitive maps (FCMs) are particularly suited to the modeling of complex social problems, such as homelessness, due to their inherent ability to model intricate, interactive systems often described in vague conceptual terms and then organize them into a specific, concrete form (i.e., the FCM) which can be readily understood by social scientists and others. Using FL we converted information, taken from recently published, peer reviewed articles, for a select group of factors related to homelessness and then calculated the strength of influence (weights) for pairs of factors. We then used these weighted relationships in a FCM to test the effects of increasing or decreasing individual or groups of factors. Results of these trials were explainable according to current empirical knowledge related to homelessness.
Prior graphic maps of homelessness have been of limited use due to the dynamic nature of the concepts related to homelessness. The FCM technique captures greater degrees of dynamism and complexity than static models, allowing relevant concepts to be manipulated and interacted. This, in turn, allows for a much more realistic picture of homelessness. Through network analysis of the FCM we determined that Education exerts the greatest force in the model and hence impacts the dynamism and complexity of a social problem such as homelessness.
The FCM built to model the complex social system of homelessness reasonably represented reality for the sample scenarios created. This confirmed that the model worked and that a search of peer reviewed, academic literature is a reasonable foundation upon which to build the model. Further, it was determined that the direction and strengths of relationships between concepts included in this map are a reasonable approximation of their action in reality. However, dynamic models are not without their limitations and must be acknowledged as inherently exploratory.
Homelessness; Complex social system; Fuzzy logic; Fuzzy Cognitive Map; Network analysis
To develop a decision support system to discriminate the diagnoses of alterations in urinary elimination, according to the nursing terminology of NANDA International (NANDA-I).
A fuzzy cognitive map (FCM) was structured considering six possible diagnoses: stress urinary incontinence, reflex urinary incontinence, urge urinary incontinence, functional urinary incontinence, total urinary incontinence and urinary retention; and 39 signals associated with them. The model was implemented in Microsoft Visual C++® Edition 2005 and applied in 195 real cases. Its performance was evaluated through the agreement test, comparing its results with the diagnoses determined by three experts (nurses). The sensitivity and specificity of the model were calculated considering the expert’s opinion as a gold standard. In order to compute the Kappa’s values we considered two situations, since more than one diagnosis was possible: the overestimation of the accordance in which the case was considered as concordant when at least one diagnoses was equal; and the underestimation of the accordance, in which the case was considered as discordant when at least one diagnosis was different.
The overestimation of the accordance showed an excellent agreement (kappa = 0.92, p < 0.0001); and the underestimation provided a moderate agreement (kappa = 0.42, p < 0.0001). In general the FCM model showed high sensitivity and specificity, of 0.95 and 0.92, respectively, but provided a low specificity value in determining the diagnosis of urge urinary incontinence (0.43) and a low sensitivity value to total urinary incontinence (0.42).
The decision support system developed presented a good performance compared to other types of expert systems for differential diagnosis of alterations in urinary elimination. Since there are few similar studies in the literature, we are convinced of the importance of investing in this kind of modeling, both from the theoretical and from the health applied points of view.
In spite of the good results, the FCM should be improved to identify the diagnoses of urge urinary incontinence and total urinary incontinence.
Fuzzy logic; Urinary incontinence; Nursing diagnosis; Differential diagnosis
Aphasia diagnosis is particularly challenging due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease.
Fuzzy probability is proposed here as the basic framework for handling the uncertainties in medical diagnosis and particularly aphasia diagnosis. To efficiently construct this fuzzy probabilistic mapping, statistical analysis is performed that constructs input membership functions as well as determines an effective set of input features.
Considering the high sensitivity of performance measures to different distribution of testing/training sets, a statistical t-test of significance is applied to compare fuzzy approach results with NN results as well as author's earlier work using fuzzy logic. The proposed fuzzy probability estimator approach clearly provides better diagnosis for both classes of data sets. Specifically, for the first and second type of fuzzy probability classifiers, i.e. spontaneous speech and comprehensive model, P-values are 2.24E-08 and 0.0059, respectively, strongly rejecting the null hypothesis.
The technique is applied and compared on both comprehensive and spontaneous speech test data for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. Statistical analysis confirms that the proposed approach can significantly improve accuracy using fewer Aphasia features.
Aphasia; fuzzy probability; fuzzy logic; medical diagnosis; fuzzy rules
Last years' mapping of diverse genomes has generated huge amounts of biological data which are currently dispersed through many databases. Integration of the information available in the various databases is required to unveil possible associations relating already known data. Biological data are often imprecise and noisy. Fuzzy set theory is specially suitable to model imprecise data while association rules are very appropriate to integrate heterogeneous data.
In this work we propose a novel fuzzy methodology based on a fuzzy association rule mining method for biological knowledge extraction. We apply this methodology over a yeast genome dataset containing heterogeneous information regarding structural and functional genome features. A number of association rules have been found, many of them agreeing with previous research in the area. In addition, a comparison between crisp and fuzzy results proves the fuzzy associations to be more reliable than crisp ones.
An integrative approach as the one carried out in this work can unveil significant knowledge which is currently hidden and dispersed through the existing biological databases. It is shown that fuzzy association rules can model this knowledge in an intuitive way by using linguistic labels and few easy-understandable parameters.
The diagnosis of many diseases can be often formulated as a decision problem; uncertainty affects these problems so that many computerized Diagnostic Decision Support Systems (in the following, DDSSs) have been developed to aid the physician in interpreting clinical data and thus to improve the quality of the whole process. Fuzzy logic, a well established attempt at the formalization and mechanization of human capabilities in reasoning and deciding with noisy information, can be profitably used. Recently, we informally proposed a general methodology to automatically build DDSSs on the top of fuzzy knowledge extracted from data.
We carefully refine and formalize our methodology that includes six stages, where the first three stages work with crisp rules, whereas the last three ones are employed on fuzzy models. Its strength relies on its generality and modularity since it supports the integration of alternative techniques in each of its stages.
The methodology is designed and implemented in the form of a modular and portable software architecture according to a component-based approach. The architecture is deeply described and a summary inspection of the main components in terms of UML diagrams is outlined as well. A first implementation of the architecture has been then realized in Java following the object-oriented paradigm and used to instantiate a DDSS example aimed at accurately diagnosing breast masses as a proof of concept.
The results prove the feasibility of the whole methodology implemented in terms of the architecture proposed.
As an innovative as well as an interdisciplinary research project, this study performed an analysis of brain signals so as to establish BrainIC as an auxiliary tool for physician diagnosis. Cognition behavior sciences, embedded technology, system on chips (SOC) design and physiological signal processing are integrated in this work. Moreover, a chip is built for real-time electroencephalography (EEG) processing purposes and a Brain Electrical Activity Mapping (BEAM) system, and a knowledge database is constructed to diagnose psychosis and body challenges in learning various behaviors and signals antithesis by a fuzzy inference engine. This work is completed with a medical support system developed for the mentally disabled or the elderly abled.
embedded system; system on chip; FPGA; physiology signal; BEAM; EEG
This work presents a new approach for collaboration among sensors in Wireless Sensor Networks. These networks are composed of a large number of sensor nodes with constrained resources: limited computational capability, memory, power sources, etc. Nowadays, there is a growing interest in the integration of Soft Computing technologies into Wireless Sensor Networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks. The objective of this work is to design a collaborative knowledge-based network, in which each sensor executes an adapted Fuzzy Rule-Based System, which presents significant advantages such as: experts can define interpretable knowledge with uncertainty and imprecision, collaborative knowledge can be separated from control or modeling knowledge and the collaborative approach may support neighbor sensor failures and communication errors. As a real-world application of this approach, we demonstrate a collaborative modeling system for pests, in which an alarm about the development of olive tree fly is inferred. The results show that knowledge-based sensors are suitable for a wide range of applications and that the behavior of a knowledge-based sensor may be modified by inferences and knowledge of neighbor sensors in order to obtain a more accurate and reliable output.
Wireless Sensor Networks; Fuzzy Rule-Based System; Cooperating Objects
Background. Bacterial meningitis is a life-threatening medical emergency that requires urgent diagnosis and treatment. Diagnosis is infrequently missed if the patient presents with the classic symptoms of fever, headache, rash, nuchal rigidity, or Kernig or Brudzinski sign. However, it may be less obvious in neonates, elderly, or immunocompromised patients. Meningitis which presents as isolated torticollis, without any other signs or symptoms, is exceedingly rare. Objective. To identify an abnormal presentation of meningitis in an adult immunocompromised patient. Case Report. We present a case of an adult diabetic male who presented multiple times to the ED with complaint of isolated torticollis, who ultimately was diagnosed with bacterial meningitis. Conclusion. We propose that in the absence of sufficient explanation for acute painful torticollis in an immunocompromised adult patient, further evaluation, possibly including a lumbar puncture may be warranted.
Classical mathematical models for medical diagnosis which have been computerized are known to perform very poorly when compared to diagnoses made by the physician. Factors which contribute to their poor performance relate to the omission by these models of important information on the patient such as symptoms of past undiagnosed diseases which can only be vaguely recalled by the patient. Other deficiencies include failure to model the stage of development of the disease and certain intrinsically fuzzy aspects of the pertinent information nets that are needed to develop a medical hypothesis. Models which attempted to remedy these shortcomings were developed and presented by the authors elsewhere.
In this effort, we describe a study in which our fuzzy diagnosis models were computerized, validated and compared with a mock physician hypothesis as well as existing mathematical models. The example involved a medical hypothsis concerning a medical condition of valvular heart disease. The results show that while there were discrepancies between the fuzzy model's and the physician's hypotheses, the model's performance was vastly superior to that of existing mathematical models.
In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images.
This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain.
The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system.
The quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain.
Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related.
Results indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use.
Children with diabetes may be managed by either paediatricians or adult physicians with a particular interest in diabetes. This study compares the views of these two groups of doctors on juvenile onset diabetes. A questionnaire was given to all doctors attending two conferences, one primarily for paediatricians and one primarily for adult physicians with a particular interest in diabetes. Adult physicians estimated morbidity and mortality from juvenile onset diabetes to be significantly higher after 30 years than did paediatricians. The two groups of doctors also differed in the target blood glucose concentrations they considered optimal for diabetic children--more paediatricians opted for higher values than did adult physicians. The findings of this study support the view that paediatricians and adult physicians view juvenile onset diabetes differently. The origin of these differences is uncertain but may relate to the contrasting clinical experiences of the two groups of specialists.
Meningeal carcinomatosis without gross tumour in the substance of the brain or spinal cord has been reported rarely. Two cases observed at the Victoria General Hospital, Halifax, presented a bizarre clinical picture consisting of signs of meningeal irritation without fever, and psychotic behaviour. Examination of the cerebrospinal fluid revealed low sugar concentration and increased pressure, protein and cells. In one case these cells were readily identified as malignant on stained smears. At autopsy the surfaces of the cerebral hemispheres, cerebellum and brain stem were covered by an opalescent film and on section the subarachnoid space was densely packed with malignant cells. Both primary tumours were adenocarcinomas, one originating in the gallbladder and one in the rectum. The diagnosis of meningeal carcinomatosis must be considered in patients presenting with profound mental changes and meningeal irritation without fever. Diagnosis may be confirmed by cytological examination of the cerebrospinal fluid. The primary tumour is most commonly an adenocarcinoma. There is no satisfactory treatment available.
The purpose of this study was to establish audiology referral protocols for post meningitis paediatric populations in two academic hospitals in Gauteng, South Africa. Specific objectives of this study included determining if audiological assessment referrals were made following infection; determining the time of referral post meningitis diagnosis; establishing what audiological assessments were conducted on this population, as well as determining any correlations between signs and symptoms of meningitis and referrals for audiology assessments. Medical records of 47 children admitted to hospital with a diagnosis of meningitis between the ages of birth and 6 years were reviewed following a retrospective record review design. Data relevant to the current study were obtained from hospital records and this was captured in a data spreadsheet. Both descriptive and inferential statistics were implemented in analysis of the data. Inferential statistics in the form of logistic regression analysis was used to establish any significant factor that may predict referral for audiological assessment. The findings indicated that almost half (40%) of the cases were not referred for audiological services. Of those cases referred for assessment, 89% were referred as in-patients before hospital discharge, with minimal referrals occurring after discharge from hospital. Screening, rather than diagnostic audiology measures were conducted on a majority of the cases. Logistic regression analysis identified fever as the only predictor variable (p<0.01) for audiological assessment referral. Results from this study highlight the need for the establishment of audiology referral protocols for paediatric meningitis populations to ensure that early identification and early intervention occurs.
Paediatric meningitis; hospital audiology referral protocols; audiology referral rates
Intelligent management of wearable applications in rehabilitation requires an understanding of the current context, which is constantly changing over the rehabilitation process because of changes in the person's status and environment. This paper presents a dynamic recurrent neuro-fuzzy system that implements expert-and evidence-based reasoning. It is intended to provide context-awareness for wearable intelligent agents/assistants (WIAs).
The model structure includes the following types of signals: inputs, states, outputs and outcomes. Inputs are facts or events which have effects on patients' physiological and rehabilitative states; different classes of inputs (e.g., facts, context, medication, therapy) have different nonlinear mappings to a fuzzy "effect." States are dimensionless linguistic fuzzy variables that change based on causal rules, as implemented by a fuzzy inference system (FIS). The FIS, with rules based on expertise and evidence, essentially defines the nonlinear state equations that are implemented by nuclei of dynamic neurons. Outputs, a function of weighing of states and effective inputs using conventional or fuzzy mapping, can perform actions, predict performance, or assist with decision-making. Outcomes are scalars to be extremized that are a function of outputs and states.
The first example demonstrates setup and use for a large-scale stroke neurorehabilitation application (with 16 inputs, 12 states, 5 outputs and 3 outcomes), showing how this modelling tool can successfully capture causal dynamic change in context-relevant states (e.g., impairments, pain) as a function of input event patterns (e.g., medications). The second example demonstrates use of scientific evidence to develop rule-based dynamic models, here for predicting changes in muscle strength with short-term fatigue and long-term strength-training.
A neuro-fuzzy modelling framework is developed for estimating rehabilitative change that can be applied in any field of rehabilitation if sufficient evidence and/or expert knowledge are available. It is intended to provide context-awareness of changing status through state estimation, which is critical information for WIA's to be effective.
Although the coexistence of bacterial meningitis and arthritis has been noted in several studies, it remains unclear how often both conditions occur simultaneously.
We evaluated the presence of arthritis in a prospective nationwide cohort of 696 episodes of community-acquired bacterial meningitis, confirmed by culture of cerebrospinal fluid, which occurred in patients aged >16 years. The diagnosis of arthritis was based upon the judgment of the treating physician. To identify differences between groups Fisher exact statistics and the Mann-Whitney U test were used.
Arthritis was recorded in 48 of 696 (7%) episodes of community-acquired bacterial meningitis in adults. Joint-fluid aspirations were performed in 23 of 48 patients (48%) and joint-fluid cultures yielded bacteria in 6 of 23 patients (26%). Arthritis occurred most frequently in patients with meningococcal meningitis (12%). Of the 48 patients with bacterial meningitis and coexisting arthritis, four died (8%) and 10 (23%) had residual joint symptoms.
Arthritis is a common manifestation in patients with community-acquired bacterial meningitis. Functional outcome of arthritis in bacterial meningitis is generally good because meningococcal arthritis is usually immune-mediated, and pneumococcal arthritis is generally less deforming than staphylococcal arthritis. Nevertheless, additional therapeutic measures should be considered if clinical course is complicated by arthritis. In patients with infectious arthritis prolonged antibiotic therapy is mandatory.
Computerized Clinical Practice Guidelines (CPGs) improve quality of care by assisting physicians in their decision making. A number of problems emerges since patients with close characteristics are given contradictory recommendations. In this article, we propose to use fuzzy logic to model uncertainty due to the use of thresholds in CPGs. A fuzzy classification procedure has been developed that provides for each message of the CPG, a strength of recommendation that rates the appropriateness of the recommendation for the patient under consideration. This work is done in the context of a CPG for the diagnosis and the management of hypertension, published in 1997 by the French agency ANAES. A population of 82 patients with mild to moderate hypertension was selected and the results of the classification system were compared to whose given by a classical decision tree. Observed agreement is 86.6% and the variability of recommendations for patients with close characteristics is reduced.
While the past century of neuroscientific research has brought considerable progress in defining the boundaries of the human cerebral cortex, there are cases in which the demarcation of one area from another remains fuzzy. Despite the existence of clearly demarcated areas, examples of gradual transitions between areas are known since early cytoarchitectonic studies. Since multi-modal anatomical approaches and functional connectivity studies brought renewed attention to the topic, a better understanding of the theoretical and methodological implications of fuzzy boundaries in brain science can be conceptually useful. This article provides a preliminary conceptual framework to understand this problem by applying philosophical theories of vagueness to three levels of neuroanatomical research. For the first two levels (cytoarchitectonics and fMRI studies), vagueness will be distinguished from other forms of uncertainty, such as imprecise measurement or ambiguous causal sources of activation. The article proceeds to discuss the implications of these levels for the anatomical study of connectivity between cortical areas. There, vagueness gets imported into connectivity studies since the network structure is dependent on the parcellation scheme and thresholds have to be used to delineate functional boundaries. Functional connectivity may introduce an additional form of vagueness, as it is an organizational principle of the brain. The article concludes by discussing what steps are appropriate to define areal boundaries more precisely.
connectivity; cytoarchitectonics; fuzzy boundaries; neuroanatomy; statistical thresholding; vagueness
Acute urinary retention in aseptic meningitis is rarely encountered, and the diagnosis of aseptic meningitis may be less than straightforward, because its symptoms and neurological signs are occasionally mild or absent. We report a case in which acute urinary retention provided an appropriate indication for the diagnosis of aseptic meningitis as the cause of an undiagnosed fever.
Acute urinary retention; Meningitis-retention syndrome; Aseptic meningitis
Proinflammatory cytokines have been shown to impair cognition; consequently, immune activity in the central nervous system was considered detrimental to cognitive function. Unexpectedly, however, T cells were recently shown to support learning and memory, though the underlying mechanism was unclear. We show that one of the steps in the cascade of T cell–based support of learning and memory takes place in the meningeal spaces. Performance of cognitive tasks led to accumulation of IL-4–producing T cells in the meninges. Depletion of T cells from meningeal spaces skewed meningeal myeloid cells toward a proinflammatory phenotype. T cell–derived IL-4 was critical, as IL-4−/− mice exhibited a skewed proinflammatory meningeal myeloid cell phenotype and cognitive deficits. Transplantation of IL-4−/− bone marrow into irradiated wild-type recipients also resulted in cognitive impairment and proinflammatory skew. Moreover, adoptive transfer of T cells from wild-type into IL-4−/− mice reversed cognitive impairment and attenuated the proinflammatory character of meningeal myeloid cells. Our results point to a critical role for T cell–derived IL-4 in the regulation of cognitive function through meningeal myeloid cell phenotype and brain-derived neurotrophic factor expression. These findings might lead to the development of new immune-based therapies for cognitive impairment associated with immune decline.
There are a number of obstacles to successful operationalization of clinical practice guidelines, including the difficulty in accurately representing a statement's decidability or an action's executability. Both require reasoning with incomplete and imprecise information, and we present one means of processing such information. We begin with a brief overview of fuzzy set theory, in which elements can have partial memberships in multiple sets. With fuzzy inferencing, these sets can be combined to create multiple conclusions, each with varying degrees of truth. We demonstrate a fuzzy model developed from a published clinical practice guideline on the management of first simple febrile seizures. Although the creation of fuzzy sets can be an arbitrary process, we believe that fuzzy inferencing is an effective tool for the expression of guideline recommendations, and that it can be useful for the management of imprecision and uncertainty.
Meningitis, when caused by the fungal mycoses Cryptococcus neoformans, is normally seen in immunocompromised hosts. However, immunocompetent patients are also susceptible to cryptococcal meningitis (CM). In patients with an intact immune system, CM usually presents with the typical signs and symptoms of meningitis: fever, stiff neck, and headache. Major implications for the primary and advanced practice nursing plans of carefor CM patients include a thorough history and physical exam, early diagnosis and treatment, and an individualized plan of care focused on minimizing sequelae and side effects of treatment and maximizing functional recovery.
The most common cause of eosinophilic meningitis is the rat lung worm Angiostrongylus cantonensis, a parasite which is endemic in the South East Asian and Pacific regions. While the typical clinical presentation is that of meningitis associated with an eosinophilic pleocytosis, a 45 year old man presented with a radiculomyelopathy, associated with an eosinophilic pleocytosis and cerebrospinal fluid antibodies to A. cantonensis but without signs or symptoms of meningitis. A worm was demonstrated on both computed tomographic myelography and magnetic resonance imaging scan of the spinal cord.
Percutaneous transluminal angioplasty (PTRA) has emerged as a promising treatment for patients with renovascular hypertension. However, the benefit of this procedure is hampered by restenosis that frequently occurs within around 6 months after a successful angioplasty. This paper presents a fuzzy classification system based on a fuzzy pattern matching model that is being developed to evaluate the risk of short-term restenosis. First, identified classes are represented by fuzzy prototypes that take into account the imprecision of the criteria. Second, the system is applied to angiographic features of given stenoses and provides the membership degree of these stenosis to the two classes "short term restenosis" or "no restenosis". The fuzzy classifier's performances have been tested on twenty two patients who underwent balloon angioplasty in the context of a French multicenter randomized trial EMMA. With the fuzzy classifier, restenosis were predicted prospectively with 100% while its sensitivity is about 73%. The fuzzy classification system is expected to become a reliable tool to predict PTRA outcomes.
This study proposes a new condition diagnosis method for rotating machinery developed using least squares mapping (LSM) and a fuzzy neural network. The non-dimensional symptom parameters (NSPs) in the time domain are defined to reflect the features of the vibration signals measured in each state. A sensitive evaluation method for selecting good symptom parameters using detection index (DI) is also proposed for detecting and distinguishing faults in rotating machinery. In order to raise the diagnosis sensitivity of the symptom parameters the synthetic symptom parameters (SSPs) are obtained by LSM. Moreover, possibility theory and the Dempster & Shafer theory (DST) are used to process the ambiguous relationship between symptoms and fault types. Finally, a sequential diagnosis method, using sequential inference and a fuzzy neural network realized by the partially-linearized neural network (PLNN), is also proposed, by which the conditions of rotating machinery can be identified sequentially. Practical examples of fault diagnosis for a roller bearing are shown to verify that the method is effective.
condition diagnosis; least squares mapping; possibility theory; Dempster & Shafer theory; fuzzy neural network
Genetic issues are important in the primary care of adolescents. A genetic diagnosis may not be made until adolescence, when the teenager presents with the first signs or symptoms of the condition. The physician’s knowledge of the natural history of a genetic disease will aid in the anticipatory guidance for teens and their parents. The physician may be called upon to advise the patient regarding hormone therapy or contraception. The paediatrician may initiate topics such as sexuality and sex education for the developmentally delayed patient. The paediatrician is also the advocate for the teenager, who must gain independence from the family in medical as well as other aspects of life. This article examines some of these issues, using cases to illustrate the genetic problems and approaches in the care of the teenaged patient.
Adolescence; Anticipatory guidance; Developmental delay; Down syndrome; Genetics; Hemophilia A; Klinefelter syndrome; Neurofibromatosis type 1; Sexuality; Turner syndrome